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Analysis of simplification in Markov state-based models for reliability assessment of complex safety systems

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  • Liang, Qingzhu
  • Yang, Yinghao
  • Zhang, Hang
  • Peng, Changhong
  • Lu, Jianchao

Abstract

One limitation of the Markov model is that the exponential growth in the number of states with increase of system complexity weakens the implementability of Markov-based state model construction for complex systems and magnifies the consumption of computational resources. This paper introduces a method for simplification in Markov state-based models for reliability assessment of complex safety systems based on decomposing the target system into independent sub-systems and adopting system-level failure rates of the sub-systems estimated individually by the developed formulas. Using failure rates of the sub-systems, a simplified model of the target system can be built easily. The proposed method was used to construct the simplified model for a typical reactor protection system found in nuclear power plants. The number of states in the simplified model is greatly reduced compared to the full model developed separately. Additionally, the results of the sensitivity analysis show that the deviation of the reliabilities of the system calculated by the simplified model and the full model is not higher than 0.0021% within the range of the model parameters; and the value of the validation metric indicates that the simplified model and the full model are in a high level of agreement.

Suggested Citation

  • Liang, Qingzhu & Yang, Yinghao & Zhang, Hang & Peng, Changhong & Lu, Jianchao, 2022. "Analysis of simplification in Markov state-based models for reliability assessment of complex safety systems," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:reensy:v:221:y:2022:i:c:s0951832022000503
    DOI: 10.1016/j.ress.2022.108373
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    5. Liu, Shuanglei & Li, Weijun & Gao, Peng & Sun, Yibo, 2022. "Modeling and performance analysis of gas leakage emergency disposal process in gas transmission station based on Stochastic Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

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